Breaking new ground in mapping human settlements from space – The Global Urban Footprint

Power-Law Distributions in Empirical Data

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Global food demand and the sustainable intensification of agriculture

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Global Change and the Ecology of Cities

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Power-law distributions in empirical data

Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,710

World Urbanization Prospects: The 1996 Revision

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A Meta-Analysis of Global Urban Land Expansion

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Global land cover mapping at 30 m resolution: A POK-based operational approach

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World Urbanization Prospects: an alternative to the UN model of projection compatible with the mobility transition theory

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TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry

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Human behavior and the principle of least effort.

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Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

Jilge, M., Heiden, U., Neumann, C., & Feilhauer, H. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.01.007 · Cited by 16

Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

Zitzlsberger, G., Podhorányi, M., Svatoň, V., Lazecký, M., & Martinovič, J. (2021) · Remote Sensing · doi:10.3390/rs13153000 · Cited by 18

Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data

Petrushevsky, N., Manzoni, M., & Guarnieri, A. M. (2021) · Remote Sensing · doi:10.3390/rs14010036 · Cited by 16

Mosaicking Copernicus Sentinel-1 Data at Global Scale

Syrris, V., Corbane, C., Pesaresi, M., & Soille, P. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2846265 · Cited by 16

Commentary: A road map for future data-driven urban planning and environmental health research

Dyer, G. M. C., Khomenko, S., Adlakha, D., Anenberg, S. C., Angelova, J., Behnisch, M., Boeing, G., Chen, X., Cirach, M., de Hoogh, K., et al. (2024) · Cities · doi:10.1016/j.cities.2024.105340 · Cited by 16

Dynamic changes of urban street thermal environment from the pedestrian perspective and their impacts on pedestrians' walking behaviors

Sang, Y., Qin, X. D., Yan, H., Wu, R., Yang, F., Middel, A., Nan, X., Zhu, L. L., & Shi, J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106971 · Cited by 15

A Review of the Machine Learning in GIS for Megacities Application

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Semi-automatic mapping of pre-census enumeration areas and population sampling frames

Qader, S. H., Lefebvre, V. A., Tatem, A. J., Pape, U. J., Himelein, K., Ninneman, A., Bengtsson, L., & Bird, T. J. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-00670-0 · Cited by 15

Built-Up Area Extraction from GF-3 SAR Data Based on a Dual-Attention Transformer Model

Li, T., Wang, C., Wu, F., Zhang, H., Tian, S., Fu, Q., & Xu, L. (2022) · Remote Sensing · doi:10.3390/rs14174182 · Cited by 15

SARBuD1.0： A SAR building dataset based on GF-3 FSII imageries for built-up area extraction with deep learning method

Wu, F., Zhang, H., Chao, W., Lu, L., Juanjuan, L., Weirong, C. H. E. N., & Zhang, B. (2022) · National Remote Sensing Bulletin · doi:10.11834/jrs.20220296 · Cited by 15

The Use of Geographic Databases for Analyzing Changes in Land Cover—A Case Study of the Region of Warmia and Mazury in Poland

Cieślak, I., Biłozor, A., Źróbek-Sokolnik, A., & Zagroba, M. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9060358 · Cited by 15

Spaceborne SAR Data for Regional Urban Mapping Using a Robust Building Extractor

Li, J., Zhang, H., Wang, C., Wu, F., & Li, L. (2020) · Remote Sensing · doi:10.3390/rs12172791 · Cited by 15

Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations

Borlaf-Mena, I., Santoro, M., Villard, L., Badea, O. N., & Tanase, M. A. (2020) · Remote Sensing · doi:10.3390/rs12183016 · Cited by 15

Mapping Local Climate Zones: A Bibliometric Meta-Analysis and Systematic ReviewPreprint

Sida, J., Huang, F., Zhan, W., Bechtel, B., Liu, Z., Demuzere, M., Huang, Y., Xu, Y., Quan, J., Xia, W., et al. (2021) · doi:10.31219/osf.io/c2bez · Cited by 15

Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data

Teillet, C., Pillot, B., Catry, T., Demagistri, L., Lyszczarz, D., Lang, M., Couteron, P., Barbier, N., Adou, K. A., Gunther, Q., et al. (2021) · Remote Sensing · doi:10.3390/rs13122398 · Cited by 14

A network optimisation approach to identify trade-offs between socio-economic and ecological objectives for regional integrated planning

Khiali-Miab, A., Grêt‐Regamey, A., Axhausen, K. W., & van Strien, M. J. (2021) · City and Environment Interactions · doi:10.1016/j.cacint.2021.100078 · Cited by 14

Cars in Latin America: An exploration of the urban landscape and street network correlates of motorization in 300 cities

Delclòs‐Alió, X., Kanai, C. M., Martins, L. S., Quistberg, D. A., Ju, Y., Dronova, I., Gouveia, N., & Rodrı́guez, D. A. (2022) · Travel Behaviour and Society · doi:10.1016/j.tbs.2022.09.005 · Cited by 14

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

Thottolil, R., Kumar, U., & Chakraborty, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-49343-3 · Cited by 18

A grid-based sample design framework for household surveys

Boo, G., Darin, É., Thomson, D. R., & Tatem, A. J. (2020) · Gates Open Research · doi:10.12688/gatesopenres.13107.1 · Cited by 14

Urban Extent Extraction Combining Sentinel Data in the Optical and Microwave Range

Iannelli, G. C., & Gamba, P. E. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2920678 · Cited by 14

High-resolution synthetic population mapping for quantifying disparities in disaster impacts: An application in the Bangladesh Coastal Zone

Rubinyi, S. L., Verschuur, J., Goldblatt, R., Gussenbauer, J., Kowarik, A., Mannix, J., Bottoms, B., & Hall, J. W. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.1033579 · Cited by 14

Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Chen, Y., Yao, S., Hu, Z., Huang, B., Miao, L., & Zhang, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3281363 · Cited by 13

Assessing Patterns and Trends in Urbanization and Land Use Efficiency Across the Philippines: A Comprehensive Analysis Using Global Earth Observation Data and SDG 11.3.1 Indicators

Santillan, J. R., & Heipke, C. (2024) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-024-00305-y · Cited by 13

Object-Based High-Rise Building Detection Using Morphological Building Index and Digital Map

Jung, S., Lee, K., & Lee, W. H. (2022) · Remote Sensing · doi:10.3390/rs14020330 · Cited by 13

ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru

Romero, N. A., Cigna, F., & Tapete, D. (2020) · Applied Sciences · doi:10.3390/app10186598 · Cited by 14

Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis

Venter, Z. S., Sadilek, A., Stanton, C., Barton, D. N., Aunan, K., Chowdhury, S., Schneider, A., & Iacus, S. M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182312567 · Cited by 13

Spatial drivers of wetland bird occupancy within an urbanized matrix in the Upper Midwestern United States

Rahlin, A. A., Saunders, S. P., & Beilke, S. (2022) · Ecosphere · doi:10.1002/ecs2.4232 · Cited by 13

Consistent metropolitan boundaries for the remote sensing of urban land

Daams, M. N., Banquet, A., Delbouve, P., & Veneri, P. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113789 · Cited by 12

Protected areas effectively resisted 33.8% of urban development pressures in China during 2000–2018

Wang, N., Du, Y., Liang, F., Yi, J., Qian, J., Tu, W., Huang, S., & Luo, P. (2023) · Applied Geography · doi:10.1016/j.apgeog.2023.103079 · Cited by 12

Building up a data engine for global urban mapping

Zhou, Y., & Weng, Q. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114242 · Cited by 14

The EEFIT Remote Sensing Reconnaissance Mission for the February 2023 Turkey Earthquakes

Voelker, B., Milillo, P., Tavakkoliestahbanati, A., Macchiarulo, V., Giardina, G., Recla, M., Schmitt, M., Cescon, M., Aktaş, Y. D., & So, E. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3476029 · Cited by 12

Exploring the spatiotemporal trends and influencing factors of human settlement suitability in Hunan province traditional villages

Zhong, Q., & Dong, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-76746-7 · Cited by 12

Urbanization: Concept, Mechanism, and Global Implications

Murayama, Y., & Estoque, R. C. (2019) · Advances in geological science · doi:10.1007/978-981-32-9224-6_19 · Cited by 12

Archaeological Sites in Small Towns—A Sustainability Assessment of Northumberland County

Vaz, E. (2020) · Sustainability · doi:10.3390/su12052018 · Cited by 12

Summertime climatic effects of urbanization and their impacts on human thermal comfort in Xiangjiang watershed, South-Central China

Wu, H., Han, L., & Li, T. (2023) · Urban Climate · doi:10.1016/j.uclim.2023.101582 · Cited by 12

Deep multitask learning with label interdependency distillation for multicriteria street-level image classification

Pelizari, P. A., Geiß, C., Groth, S., & Taubenböck, H. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.09.001 · Cited by 13

Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks

Li, Y., Matgen, P., & Chini, M. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104524 · Cited by 15

Universal patterns of intra-urban morphology: Defining a global typology of the urban fabric using unsupervised clustering

Debray, H., Gassilloud, M., Lemoine-Rodríguez, R., Wurm, M., Zhu, X.-X., & Taubenböck, H. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104610 · Cited by 15

Establishing an operational and continuous monitoring of global built-up surfaces with the Copernicus Global Human Settlement Layer

Melchiorri, M., & Kemper, T. (2023) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse57346.2023.10144201 · Cited by 11

EU cohesion policy on the ground: Analyzing small-scale effects using satellite data

Bachtrögler, J., Dolls, M., Krolage, C., Schüle, P., Taubenböck, H., & Weigand, M. (2023) · Regional Science and Urban Economics · doi:10.1016/j.regsciurbeco.2023.103954 · Cited by 11

Cultural Landmarks and Urban Landscapes in Three Contrasting Societies

Chen, J., Park, H., Fan, P., Tian, L., Ouyang, Z., & Lafortezza, R. (2021) · Sustainability · doi:10.3390/su13084295 · Cited by 11

A multi-sensor approach for characterising human-made structures by estimating area, volume and population based on sentinel data and deep learning

Fibæk, C. S., Keßler, C., & Arsanjani, J. J. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102628 · Cited by 11

Screening the stones of Venice: Mapping social perceptions of cultural significance through graph-based semi-supervised classification

Bai, N., Nourian, P., Luo, R., Cheng, T., & Roders, A. P. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.07.018 · Cited by 12

NO2 Air Pollution Trends and Settlement Growth in Megacities

Erbertseder, T., Taubenböck, H., Esch, T., Gilardi, L., Paeth, H., & Dech, S. W. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3419573 · Cited by 10

A FREE AND OPEN SOURCE TOOL TO ASSESS THE ACCURACY OF LAND COVER MAPS: IMPLEMENTATION AND APPLICATION TO LOMBARDY REGION (ITALY)

Bratić, G., Brovelli, M. A., & Molinari, M. E. (2018) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-87-2018 · Cited by 10

Detailed urban surface characterization using spectra from enhanced spatial resolution Sentinel-2 imagery and a hierarchical multiple endmember spectral mixture analysis approach

Tzelidi, D., Stagakis, S., Mitraka, Z., & Chrysoulakis, N. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.016514 · Cited by 10

Bi- and three-dimensional urban change detection using sentinel-1 SAR temporal series

Che, M., & Gamba, P. E. (2020) · GeoInformatica · doi:10.1007/s10707-020-00398-8 · Cited by 10

Mapping Impacts of Human Activities from Nighttime Light on Vegetation Cover Changes in Southeast Asia

Xia, N., Li, M., & Cheng, L. (2021) · Land · doi:10.3390/land10020185 · Cited by 10

An Integrated Multi-Source Dataset for Measuring Settlement Evolution in the United States from 1810 to 2020

Ahn, Y., Leyk, S., Uhl, J. H., & McShane, C. M. (2024) · Scientific Data · doi:10.1038/s41597-024-03081-x · Cited by 10

PRISI: A novel piecewise radar impervious surface index for urban monitoring using Sentinel-1 data

Ding, Y., Ding, Q., Yang, J., Shao, Z., & Huang, X. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.103033 · Cited by 9

Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques

Blasco, J. M. D., Cian, F., Hanssen, R. F., & Verstraeten, G. (2020) · Remote Sensing · doi:10.3390/rs12030584 · Cited by 9

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States

Uhl, J. H., & Leyk, S. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103469 · Cited by 9

Towards a Sentinel-2 Based Human Settlement Layer

Helber, P., Bischke, B., Hees, J., & Dengel, A. R. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898172 · Cited by 9

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

Koebe, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241981 · Cited by 9

So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale

Doda, S., Wang, Y., Kahl, M., Hoffmann, E. J., Ouan, K., Taubenböck, H., & Zhu, X. X. (2022) · Scientific Data · doi:10.1038/s41597-022-01780-x · Cited by 9

Assessing the impact of urbanization on forest carbon stocks and social costs using a machine learning approach

Chang, D., Jeong, S., & Shin, J. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176521 · Cited by 8

Global spatially-distributed sectoral GDP map for disaster risk analysis

Shoji, T., Kajiyama, K., Yamazaki, D., Kita, Y., & Watanabe, M. (2025) · Earth system science data · doi:10.5194/essd-17-6669-2025 · Cited by 8

Residential Segregation Between Income Groups in International Perspective

Comandon, A., & Veneri, P. (2021) · The urban book series · doi:10.1007/978-3-030-64569-4_2 · Cited by 9

Spatial characteristics of physical environments for human settlements in Jinsha River watershed (Yunnan section), China

Xia, J., & Dong, P. (2019) · Geomatics Natural Hazards and Risk · doi:10.1080/19475705.2018.1532461 · Cited by 8

Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method

Li, L., Zhu, J., Cheng, G., & Zhang, B. (2021) · Remote Sensing · doi:10.3390/rs13204073 · Cited by 8

The use of combined Landsat and Radarsat data for urban ecosystem accounting in Canada

Grenier, M., Lantz, N., Soulard, F., & Wang, J. (2020) · Statistical Journal of the IAOS · doi:10.3233/sji-200663 · Cited by 8

A Downscaling Approach to Compare COVID-19 Count Data from Databases Aggregated at Different Spatial Scales

Python, A., Bender, A., Blangiardo, M., Illian, J., Lin, Y., Liu, B., Lucas, T. C. D., Tan, S., Wen, Y., Svanidze, D., et al. (2021) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12738 · Cited by 8

Rethinking urban environments and health

Tonne, C. C. (2023) · Epidemiology · doi:10.1097/ede.0000000000001606 · Cited by 8

A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia

Qader, S. H., Lefebvre, V. A., Ninneman, A., Himelein, K., Pape, U. J., Bengtsson, L., Tatem, A., & Bird, T. J. (2019) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-8972 · Cited by 8

Identifying Urban Areas by Combining Human Judgment and Machine Learning: An Application to India

Galdo, V., Li, Y. S., & Rama, M. (2020) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-9160 · Cited by 10

Quantifying Carbon Stock Variability of Species Within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery

Mngadi, M., Odindi, J., & Mutanga, O. (2022) · doi:10.1002/9781119788157.ch7 · Cited by 8

Assessment, monitoring, and early warning of droughts: the potential for satellite remote sensing and beyond

Graw, V., Dubovyk, O., Duguru, M., Heid, P., Ghazaryan, G., de León, J. C. V., Post, J., Szarzynski, J., Tsegai, D., & Walz, Y. (2019) · Current directions in water scarcity research · doi:10.1016/b978-0-12-814820-4.00009-2 · Cited by 7

Spatial Analysis of Settlement Structures to Identify Pattern Formation Mechanisms in Inter-Urban Systems

Henn, K., Friesen, J., Hartig, J., & Pelz, P. F. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9090541 · Cited by 7

Designing a Validation Protocol for Remote Sensing Based Operational Forest Masks Applications. Comparison of Products Across Europe

Fernández-Carrillo, Á., Franco-Nieto, A., Pinto-Bañuls, E., Basarte-Mena, M., & Revilla-Romero, B. (2020) · Remote Sensing · doi:10.3390/rs12193159 · Cited by 7

Deciphering the many maps of the Xingu River Basin – an assessment of land cover classifications at multiple scales

Kalácska, M., Arroyo‐Mora, J. P., Lucanus, O., de Sousa, L. M., Pereira, T. D. S., & Vieira, T. B. (2020) · Proceedings of the Academy of Natural Sciences of Philadelphia · doi:10.1635/053.166.0118 · Cited by 6

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning

Mendes, A., Palmer, T., Berens, A. S., Espey, J., Price, R., Mallya, A., Brown, S., Martinez, M., Farag, N. H., & Kaplan, B. (2021) · International Journal of Health Geographics · doi:10.1186/s12942-021-00277-x · Cited by 8

TRDet: Two-Stage Rotated Detection of Rural Buildings in Remote Sensing Images

Peng, B., Ren, D., Zheng, C., & Lu, A. (2022) · Remote Sensing · doi:10.3390/rs14030522 · Cited by 7

Global scaling of urban air quality

Wurm, M., Erbertseder, T., Weigand, M., & Taubenböck, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333902 · Cited by 6

Comparative analysis of different machine learning algorithms for urban footprint extraction in diverse urban contexts using high-resolution remote sensing imagery

Gui, B., Bhardwaj, A., & Sam, L. (2025) · Journal of Geographical Sciences · doi:10.1007/s11442-025-2339-y · Cited by 6

Automatic Extraction of Built-Up Areas from Very High-Resolution Satellite Imagery Using Patch-Level Spatial Features and Gestalt Laws of Perceptual Grouping

Chen, Y., Lv, Z., Huang, B., Zhang, P., & Zhang, Y. (2019) · Remote Sensing · doi:10.3390/rs11243022 · Cited by 6

A scaling investigation of urban form features in Latin America cities

Paiva, A. S. S., dos Santos, G. F., de Castro, C. P., Rodrı́guez, D. A., Bilal, U., Filho, J. F. D. S., Freitas, A., Montes, F., Dronova, I., Barreto, M. L., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293518 · Cited by 6

Earth Observation for Settlement Mapping of Amazonian Indigenous Populations to Support SDG7

Muro, J., Zurita-Arthos, L., Jara-Alvear, J., Calderón, E., Resl, R., Rienow, A., & Graw, V. (2020) · Resources · doi:10.3390/resources9080097 · Cited by 7

Remote Sensing-Based Methodology for the Quick Update of the Assessment of the Population Exposed to Natural Hazards

Boni, G., Angeli, S. D., Taramasso, A. C., & Roth, G. (2020) · Remote Sensing · doi:10.3390/rs12233943 · Cited by 6

Dataset of building locations in Poland in the 1970s and 1980s

Szubert, P., Kaim, D., & Kozak, J. (2024) · Scientific Data · doi:10.1038/s41597-024-03179-2 · Cited by 6

Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation

Liu, Z., Zhang, Z., Meng, Y. Q., & Tang, P. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2353110 · Cited by 5

Instance-Aware Contour Learning for Vectorized Building Extraction From Remote Sensing Imagery

Huang, X., Chen, K., Wang, Z., & Sun, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3425725 · Cited by 5

Multifunctional Identification and Transition Path of Rural Homesteads: A Case Study of Jilin Province

Wang, X., Wu, S., Wang, D., & Wei, Y. (2024) · Land · doi:10.3390/land13101590 · Cited by 5

Typhoon Rainfall Intensification by Urban Dynamical Effects in Shanghai

Yue, C., Liu, C., Deng, L., Han, Z., Wang, X., Yao, Y., Gu, W., Tang, Y., Ao, X., Ma, B., et al. (2025) · Geophysical Research Letters · doi:10.1029/2025gl116313 · Cited by 5

Evaluating the quality of rural human settlements and the perception of farm households—A case study of traditional agricultural areas in China

Ma, Y., Han, D., Qiao, J., Ma, J., Ma, Y., Wang, Z., Guo, P., Hui, X., & Xu, J. (2026) · Habitat International · doi:10.1016/j.habitatint.2026.103724 · Cited by 5

Urban heat mitigation and adaptation: the state of the art

Ulpiani, G., Yenneti, K., Pigliautile, I., Pisello, A. L., Martilli, A., Bartesaghi-Koc, C., Fabiani, C., Treville, A., Vetters, N., & Bertoldi, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13502-6.00002-6 · Cited by 5

Cultural Heritages Lead to Less Dense and Greener Cities—Evidence from 371 Chinese Cities

Zhang, Z., Cheng, R., Dan, Y., & Wang, L. (2025) · Land · doi:10.3390/land14010177 · Cited by 5

Characterizing uncertainty of general building stock exposure data

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Optimization Approaches of Multi-Dimensional Environments in Rural Space Reproduction Driven by Tourism

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